Generation of stable entanglement between two cavity mirrors by squeezed-reservoir engineering

The generation of quantum entanglement of macroscopic or mesoscopic bodies in mechanical motion is generally bounded by the thermal fluctuation exerted by their environments. Here we propose a scheme to establish stationary entanglement between two mechanically oscillating mirrors of a cavity. It is revealed that, by applying a broadband squeezed laser acting as a squeezed-vacuum reservoir to the cavity, a stable entanglement between the mechanical mirrors can be generated. Using the adiabatic elimination and master equation methods, we analytically find that the generated entanglement is essentially determined by the squeezing of the relative momentum of the mechanical mirrors, which is transferred from the squeezed reservoir through the cavity. Numerical verification indicates that our scheme is within the present experimental state of the art of optomechanics.Comment: 9 pages, 6 figure

Parity-relevant Zitterbewegung and quantum simulation by a single trapped ion

Zitterbewegung (ZB), the trembling of free relativistic electrons in a vacuum could be simulated by a single trapped ion. We focus on the variations of ZB under different parity conditions and find no ZB in the case of odd or even parity. ZB occurs only for admixture of the odd and even parity states. We also show the similar role played by the parity operator for the trapped ion in Fock-state representation and the space inversion operator for a realistic relativistic electron. Although the ZB effect is invisible in a relativistic electron, preparation of the trapped ion in different parity states is a sophisticated job, which makes it possible to observe the parity relevant ZB effects with currently available techniques.Comment: 4 pages, 1 figur

Learning Feature Pyramids for Human Pose Estimation

Articulated human pose estimation is a fundamental yet challenging task in computer vision. The difficulty is particularly pronounced in scale variations of human body parts when camera view changes or severe foreshortening happens. Although pyramid methods are widely used to handle scale changes at inference time, learning feature pyramids in deep convolutional neural networks (DCNNs) is still not well explored. In this work, we design a Pyramid Residual Module (PRMs) to enhance the invariance in scales of DCNNs. Given input features, the PRMs learn convolutional filters on various scales of input features, which are obtained with different subsampling ratios in a multi-branch network. Moreover, we observe that it is inappropriate to adopt existing methods to initialize the weights of multi-branch networks, which achieve superior performance than plain networks in many tasks recently. Therefore, we provide theoretic derivation to extend the current weight initialization scheme to multi-branch network structures. We investigate our method on two standard benchmarks for human pose estimation. Our approach obtains state-of-the-art results on both benchmarks. Code is available at https://github.com/bearpaw/PyraNet.Comment: Submitted to ICCV 201

Multiplet resonance lifetimes in resonant inelastic X-ray scattering involving shallow core levels

Resonant inelastic X-ray scattering (RIXS) spectra of model copper- and nickel-based transition metal oxides are measured over a wide range of energies near the M-edge (h$\nu$=60-80eV) to better understand the properties of resonant scattering involving shallow core levels. Standard multiplet RIXS calculations are found to deviate significantly from the observed spectra. However, by incorporating the self consistently calculated decay lifetime for each intermediate resonance state within a given resonance edge, we obtain dramatically improved agreement between data and theory. Our results suggest that these textured lifetime corrections can enable a quantitative correspondence between first principles predictions and RIXS data on model multiplet systems. This accurate model is also used to analyze resonant elastic scattering, which displays the elastic Fano effect and provides a rough upper bound for the core hole shake-up response time.Comment: 6 pages, 3 figure

Multi-Context Attention for Human Pose Estimation

In this paper, we propose to incorporate convolutional neural networks with a multi-context attention mechanism into an end-to-end framework for human pose estimation. We adopt stacked hourglass networks to generate attention maps from features at multiple resolutions with various semantics. The Conditional Random Field (CRF) is utilized to model the correlations among neighboring regions in the attention map. We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on the detailed description for different body parts. Hence our model has the ability to focus on different granularity from local salient regions to global semantic-consistent spaces. Additionally, we design novel Hourglass Residual Units (HRUs) to increase the receptive field of the network. These units are extensions of residual units with a side branch incorporating filters with larger receptive fields, hence features with various scales are learned and combined within the HRUs. The effectiveness of the proposed multi-context attention mechanism and the hourglass residual units is evaluated on two widely used human pose estimation benchmarks. Our approach outperforms all existing methods on both benchmarks over all the body parts.Comment: The first two authors contribute equally to this wor

Mechanism of Exact Transition between Cationic and Anionic Redox Activities in Cathode Material Li2FeSiO4.

The discovery of anion redox activity is promising for boosting the capacity of lithium ion battery (LIB) cathodes. However, fundamental understanding of the mechanisms that trigger the anionic redox is still lacking. Here, using hybrid density functional study combined with experimental soft X-ray absorption spectroscopy (sXAS) measurements, we unambiguously proved that Li(2- x)FeSiO4 performs sequent cationic and anionic redox activity through delithiation. Specifically, Fe2+ is oxidized to Fe3+ during the first Li ion extraction per formula unit (f.u.), while the second Li ion extraction triggered the oxygen redox exclusively. Cationic and anionic redox result in electron and hole polaron states, respectively, explaining the poor conductivity of Li(2- x)FeSiO4 noted by previous experiments. In contrast, other cathode materials in this family exhibit diversity of the redox process. Li2MnSiO4 shows double cationic redox (Mn2+-Mn4+) during the whole delithiation, while Li2CoSiO4 shows simultaneous cationic and anionic redox. The present finding not only provides new insights into the oxygen redox activity in polyanionic compounds for rechargeable batteries but also sheds light on the future design of high-capacity rechargeable batteries